{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Titanic Disaster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise is based on the titanic Disaster dataset avaiable at [Kaggle](https://www.kaggle.com/c/titanic).  \n",
    "To know more about the variables check [here](https://www.kaggle.com/c/titanic/data)\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Titanic_Desaster/train.csv) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable titanic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic_Desaster/train.csv'\n",
    "\n",
    "titanic = pd.read_csv(url)\n",
    "\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Set PassengerId as the index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.set_index('PassengerId').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a pie chart presenting the male/female proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gliu3hKKi+vzww0ssWPAIixaNZfPmVixbdvmvHlNSUp2SkioAFBdXBYowpuSX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\nWlJBiYhIWvo/rI6mZkORh2MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e78690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sum the instances of males and females\n",
    "males = (titanic['Sex'] == 'male').sum()\n",
    "females = (titanic['Sex'] == 'female').sum()\n",
    "\n",
    "# put them into a list called proportions\n",
    "proportions = [males, females]\n",
    "\n",
    "# Create a pie chart\n",
    "plt.pie(\n",
    "    # using proportions\n",
    "    proportions,\n",
    "    \n",
    "    # with the labels being officer names\n",
    "    labels = ['Males', 'Females'],\n",
    "    \n",
    "    # with no shadows\n",
    "    shadow = False,\n",
    "    \n",
    "    # with colors\n",
    "    colors = ['blue','red'],\n",
    "    \n",
    "    # with one slide exploded out\n",
    "    explode = (0.15 , 0),\n",
    "    \n",
    "    # with the start angle at 90%\n",
    "    startangle = 90,\n",
    "    \n",
    "    # with the percent listed as a fraction\n",
    "    autopct = '%1.1f%%'\n",
    "    )\n",
    "\n",
    "# View the plot drop above\n",
    "plt.axis('equal')\n",
    "\n",
    "# Set labels\n",
    "plt.title(\"Sex Proportion\")\n",
    "\n",
    "# View the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the Fare payed and the Age, differ the plot color by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5, 85)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eSICq7WIHmU8Bf6qUigHHgIe11p5S6svAk/jNaZ/WWucucrrERbQa5qysZnJ/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\n+rWhg8zCobPbiHQMEEsMYTb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a678c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creates the plot using\n",
    "lm = sns.lmplot(x = 'Age', y = 'Fare', data = titanic, hue = 'Sex', fit_reg=False)\n",
    "\n",
    "# set title\n",
    "lm.set(title = 'Fare x Age')\n",
    "\n",
    "# get the axes object and tweak it\n",
    "axes = lm.axes\n",
    "axes[0,0].set_ylim(-5,)\n",
    "axes[0,0].set_xlim(-5,85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. How many people survived?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Survived.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Create a histogram with the Fare payed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1177b9e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sort the values from the top to the least value and slice the first 5 items\n",
    "df = titanic.Fare.sort_values(ascending = False)\n",
    "df\n",
    "\n",
    "# create bins interval using numpy\n",
    "binsVal = np.arange(0,600,10)\n",
    "binsVal\n",
    "\n",
    "# create the plot\n",
    "plt.hist(df, bins = binsVal)\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Fare Payed Histrogram')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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